code: def get_fc1(self, last_conv, is_train, dropout=0.4):
      body = last_conv
      if self.fc_type == "Z":
          body = paddle.static.nn.batch_norm(
              input=body,
              act=None,
              epsilon=1e-05,
              is_test=False if is_train else True,
          )
          if dropout > 0:
              body = paddle.nn.functional.dropout(
                  x=body,
                  p=dropout,
                  training=is_train,
                  mode="upscale_in_train",
              )
          fc1 = body
      elif self.fc_type == "E":
          body = paddle.static.nn.batch_norm(
              input=body,
              act=None,
              epsilon=1e-05,
              is_test=False if is_train else True,
          )
          if dropout > 0:
              body = paddle.nn.functional.dropout(
                  x=body,
                  p=dropout,
                  training=is_train,
                  mode="upscale_in_train",
              )
          fc1 = paddle.static.nn.fc(
              x=body,
              size=self.num_features,
              weight_attr=paddle.ParamAttr(
                  initializer=paddle.nn.initializer.XavierNormal(
                      fan_in=0.0
                  )
              ),
              bias_attr=paddle.ParamAttr(
                  initializer=paddle.nn.initializer.Constant()
              ),
          )
          fc1 = paddle.static.nn.batch_norm(
              input=fc1,
              act=None,
              epsilon=1e-05,
              is_test=False if is_train else True,
          )
  
      elif self.fc_type == "FC":
          body = paddle.static.nn.batch_norm(
              input=body,
              act=None,
              epsilon=1e-05,
              is_test=False if is_train else True,
          )
          fc1 = paddle.static.nn.fc(
              x=body,
              size=self.num_features,
              weight_attr=paddle.ParamAttr(
                  initializer=paddle.nn.initializer.XavierNormal(
                      fan_in=0.0
                  )
              ),
              bias_attr=paddle.ParamAttr(
                  initializer=paddle.nn.initializer.Constant()
              ),
          )
          fc1 = paddle.static.nn.batch_norm(
              input=fc1,
              act=None,
              epsilon=1e-05,
              is_test=False if is_train else True,
          )
  
      return fc1
  
file_path: insightface/recognition/arcface_paddle/static/backbones/iresnet.py
imports: import paddle
  from collections import OrderedDict
label_desc: Use image node_id 3 for calling the FresResNet obj's underlying member method code with example usage: obj.get_fc1(last_conv, is_train, dropout) and returns: fc1